Load Packages

library(tidyverse)
library(cowplot)
library(ggthemes)

library(splatter)
library(scater)
library(scran)
library(scRNAseq)

theme_set(theme_few())

all_metadata <- read_csv("scRNA_coldata.csv")

batch <- all_metadata[!is.na(str_extract(all_metadata$vars, regex("batch", ignore_case = T))), ]
tech <- all_metadata[!is.na(str_extract(all_metadata$vars, regex("tech", ignore_case = T))), ]

bind_rows(batch, tech) %>% distinct(data)

## # A tibble: 10 × 1
##    data                     
##    <chr>                    
##  1 AztekinTailData()        
##  2 BacherTCellData()        
##  3 CampbellBrainData()      
##  4 GiladiHSCData(mode='rna')
##  5 LedergorMyelomaData()    
##  6 KotliarovPBMCData()      
##  7 MessmerESCData()         
##  8 PaulHSCData()            
##  9 ZilionisLungData('mouse')
## 10 WuKidneyData()

Aztekin Tail Data

AztekinTailData <- AztekinTailData()
AztekinTailData

## class: SingleCellExperiment 
## dim: 31535 13199 
## metadata(0):
## assays(1): counts
## rownames(31535): Xelaev18000001m.g Xelaev18000003m.g ... loc398467.S
##   Xetrov90026938m.S
## rowData names(0):
## colnames(13199): AAACCTGAGCTAGTTC.1 AAACCTGGTGGGTCAA.1 ...
##   TTGTAGGCAGTACACT.1 TTTGCGCAGCGTGAAC.1.1
## colData names(9): cell sample ... Condition batch
## reducedDimNames(1): UMAP
## mainExpName: NULL
## altExpNames(0):

table(Batch = colData(AztekinTailData)$batch)

## Batch
##    1    2    3    4 
## 1552 3277 2354 6016

table(Cluster = colData(AztekinTailData)$cluster)

## Cluster
##              Alpha ionocyte          Anterior notochord 
##                          56                          34 
##               Beta ionocyte                Dermomyotome 
##                         207                          84 
##     Differentiating myocyte      Differentiating neuron 
##                          12                         158 
##        Dopaminergic neurons                   Epidermis 
##                          80                        1800 
##               Erythrocyte 1               Erythrocyte 2 
##                        2761                         132 
##               Erythrocyte 3               Erythrocyte 4 
##                         982                         862 
##                 Floor plate                 Goblet cell 
##                         109                        1754 
##               Interneuron 1               Interneuron 2 
##                         128                          73 
##               Interneuron 3               Interneuron 4 
##                          24                          23 
##      laminin-rich epidermis Lymphoid 1 (Gata2-, Gata3+) 
##                          21                          56 
##         Lymphoid 2 (Cxcr6+)                  Lymphoid 3 
##                          65                          31 
## Lymphoid 4 (Gata2+, Gata3-)          Lymphoid 5 (CD19+) 
##                          77                          14 
##  Lymphoid endothelial cells                  Melanocyte 
##                          12                          25 
##        Melanocyte precursor        Melanocyte stem cell 
##                          21                          37 
##                  Mesenchyme                Motor neuron 
##                         215                          76 
##      Motor neuron (leptin+)                   Myeloid 1 
##                          71                         475 
##                   Myeloid 2                     Myotome 
##                         265                         303 
##             Oligodendrocyte         Posterior notochord 
##                           8                          54 
##                        ROCs              Satellite cell 
##                         254                          44 
##                  Sclerotome             Skeletal muscle 
##                         752                         117 
##        Small secretory cell               Smooth muscle 
##                          37                          11 
##      Spinal cord progenitor                   Syndetome 
##                         505                          15 
##   Vascular endothelial cell     Vulnerable Motor Neuron 
##                          75                         284

AztekinTailData <- logNormCounts(AztekinTailData)

# Subset to top 10% HVG
chosen_features <-
  getTopHVGs(modelGeneVar(AztekinTailData, AztekinTailData$batch),
             n = ceiling(nrow(AztekinTailData) / 10))
AztekinTailData <- AztekinTailData[chosen_features, ]

# Subset to two smallest batches
AztekinTailData <- AztekinTailData[, colData(AztekinTailData)$batch %in% c(1, 3)]

# Create default named factors for batches and cell clusters
colData(AztekinTailData)$Batch <- factor(colData(AztekinTailData)$batch)
colData(AztekinTailData)$Cluster <- factor(colData(AztekinTailData)$cluster)

AztekinTailData <- runPCA(AztekinTailData)
plotPCA(AztekinTailData, color_by = "Batch")

plotPCA(AztekinTailData, color_by = "Cluster", add_legend = F)

AztekinTailData <- runTSNE(AztekinTailData)
plotTSNE(AztekinTailData, color_by = "Batch")

plotTSNE(AztekinTailData, color_by = "Cluster", add_legend = F)

AztekinTailData <- runUMAP(AztekinTailData)
plotUMAP(AztekinTailData, color_by = "Batch")

plotUMAP(AztekinTailData, color_by = "Cluster", add_legend = F)

Bacher T-Cell Data

BacherTCellData <- BacherTCellData()
BacherTCellData

## class: SingleCellExperiment 
## dim: 33538 104417 
## metadata(0):
## assays(1): counts
## rownames(33538): MIR1302-2HG FAM138A ... AC213203.1 FAM231C
## rowData names(0):
## colnames(104417): AAACCTGAGAGAACAG-J09835 AAACCTGAGTGCGTGA-J09835 ...
##   TTTGGTTAGTGTTTGC-J21856 TTTGGTTTCCAACCAA-J21856
## colData names(22): orig.ident nCount_RNA ... who_class severity
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

table(Batch = colData(BacherTCellData)$batch)

## Batch
##     1     2     3     4     5    10    11    12    13    14    15    16    17 
##  2547 11585 13344 12904  3059   587  7740  9961  4623   754  1113   376   296 
##    18    19 
## 17915 17613

table(Cluster = colData(BacherTCellData)$new_cluster_names)

## Cluster
##       Central memory              Cycling      Cytotoxic / Th1 
##                18391                  606                14262 
##             Tfh-like  Transitional memory Type-1 IFN signature 
##                45732                24165                 1261

BacherTCellData <- logNormCounts(BacherTCellData)

# Subset to top 10% HVG
chosen_features <-
  getTopHVGs(modelGeneVar(BacherTCellData, BacherTCellData$batch),
             n = ceiling(nrow(BacherTCellData) / 10))
BacherTCellData <- BacherTCellData[chosen_features, ]

# Subset to three smallest batches
BacherTCellData <- BacherTCellData[, colData(BacherTCellData)$batch %in% c(10, 16, 17)]

# Create default named factors for batches and cell clusters
colData(BacherTCellData)$Batch <- factor(colData(BacherTCellData)$batch)
colData(BacherTCellData)$Cluster <- factor(colData(BacherTCellData)$new_cluster_names)

BacherTCellData <- runPCA(BacherTCellData)
plotPCA(BacherTCellData, color_by = "Batch")

plotPCA(BacherTCellData, color_by = "Cluster")

BacherTCellData <- runTSNE(BacherTCellData)
plotTSNE(BacherTCellData, color_by = "Batch")

plotTSNE(BacherTCellData, color_by = "Cluster")

BacherTCellData <- runUMAP(BacherTCellData)
plotUMAP(BacherTCellData, color_by = "Batch")

plotUMAP(BacherTCellData, color_by = "Cluster")

Campbell Brain Data

CampbellBrainData <- CampbellBrainData()
CampbellBrainData

## class: SingleCellExperiment 
## dim: 26774 21086 
## metadata(0):
## assays(1): counts
## rownames(26774): 0610005C13Rik 0610007P14Rik ... Tfpi2 Trex2
## rowData names(0):
## colnames(21086): arc1_TACTAACAGTAN arc1_CCGCGAGCTCTT ...
##   MaleFed_TGACGCGTTCTT MaleFed_GGGGCTTATTGN
## colData names(11): ID group ... clust_neurons Sex_pred
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

table(Batch = colData(CampbellBrainData)$batches)

## Batch
##   b1   b2   b3   b4   b5   b6 
## 2607 2463 2288 6504 5277 1947

table(Cluster = colData(CampbellBrainData)$clust_all)

## Cluster
##   a01   a02   a03   a04   a05   a06   a07   a08   a09   a10   a11   a12   a13 
##   392   131   240    84   224   627   330   172   467    99  1184  3504    37 
##   a14   a15   a16   a17   a18   a19   a20  miss 
##   502   693   533   799 10515   150   238   165

CampbellBrainData <- logNormCounts(CampbellBrainData)

# Subset to top 10% HVG
chosen_features <-
  getTopHVGs(modelGeneVar(CampbellBrainData, CampbellBrainData$batches),
             n = ceiling(nrow(CampbellBrainData) / 10))
CampbellBrainData <- CampbellBrainData[chosen_features, ]

# Subset to two smallest batches
CampbellBrainData <- CampbellBrainData[, colData(CampbellBrainData)$batches %in% c("b3", "b6")]

# Create default named factors for batches and cell clusters
colData(CampbellBrainData)$Batch <- factor(colData(CampbellBrainData)$batches)
colData(CampbellBrainData)$Cluster <- factor(colData(CampbellBrainData)$clust_all)

CampbellBrainData <- runPCA(CampbellBrainData)
plotPCA(CampbellBrainData, color_by = "Batch")

plotPCA(CampbellBrainData, color_by = "Cluster", add_legend = F)

CampbellBrainData <- runTSNE(CampbellBrainData)
plotTSNE(CampbellBrainData, color_by = "Batch")

plotTSNE(CampbellBrainData, color_by = "Cluster", add_legend = F)

CampbellBrainData <- runUMAP(CampbellBrainData)
plotUMAP(CampbellBrainData, color_by = "Batch")

plotUMAP(CampbellBrainData, color_by = "Cluster", add_legend = F)

Messmer ESC Data

MessmerESCData <- MessmerESCData()
MessmerESCData

## class: SingleCellExperiment 
## dim: 58302 1344 
## metadata(0):
## assays(1): counts
## rownames(58302): ENSG00000223972 ENSG00000227232 ... ENSG00000277475
##   ENSG00000268674
## rowData names(1): Length
## colnames(1344): lane1_3600STDY6077864_ATCACGTT_L001_15986_1
##   lane1_3600STDY6077865_CGATGTTT_L001_15986_1 ...
##   lane1_3600STDY6184179_TGGTTGAC_L001_17592_1
##   lane1_3600STDY6184180_ACCTGCTG_L001_17592_1
## colData names(6): Source Name phenotype ... experiment batch sequencing
##   run
## reducedDimNames(0):
## mainExpName: endogenous
## altExpNames(1): ERCC

table(Batch = colData(MessmerESCData)$`experiment batch`)

## Batch
##   1   2   3 
## 192 768 384

table(Cluster = colData(MessmerESCData)$phenotype)

## Cluster
##  naive primed 
##    480    864

MessmerESCData <- logNormCounts(MessmerESCData)

# Subset to top 10% HVG
chosen_features <-
  getTopHVGs(modelGeneVar(MessmerESCData, MessmerESCData$`experiment batch`),
             n = ceiling(nrow(MessmerESCData) / 10))
MessmerESCData <- MessmerESCData[chosen_features, ]

# Create default named factors for batches and cell clusters
colData(MessmerESCData)$Batch <- factor(colData(MessmerESCData)$`experiment batch`)
colData(MessmerESCData)$Cluster <- factor(colData(MessmerESCData)$phenotype)

MessmerESCData <- runPCA(MessmerESCData)
plotPCA(MessmerESCData, color_by = "Batch")

plotPCA(MessmerESCData, color_by = "Cluster")

MessmerESCData <- runTSNE(MessmerESCData)
plotTSNE(MessmerESCData, color_by = "Batch")

plotTSNE(MessmerESCData, color_by = "Cluster")

MessmerESCData <- runUMAP(MessmerESCData)
plotUMAP(MessmerESCData, color_by = "Batch")

plotUMAP(MessmerESCData, color_by = "Cluster")

Paul HSC Data

PaulHSCData <- PaulHSCData() 
PaulHSCData

## class: SingleCellExperiment 
## dim: 25188 10368 
## metadata(0):
## assays(1): counts
## rownames(25188): 0610007L01Rik 0610007P14Rik ... mt-Nd4 rp9
## rowData names(0):
## colnames(10368): W29953 W29954 ... W76335 W76336
## colData names(13): Well_ID Seq_batch_ID ... CD34_measurement
##   FcgR3_measurement
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

table(Batch = colData(PaulHSCData)$Seq_batch_ID)

## Batch
## SB17 SB19 SB20 SB23 SB25 SB29 
## 1152 2304  768 2304 1536 2304

table(Cluster = colData(PaulHSCData)$Batch_desc)

## Cluster
##        Cebpa control             Cebpa KO        Cebpe control 
##                 1152                 2304                  384 
##             Cebpe KO             CMP CD41     CMP Flt3+ Csf1r+ 
##                  768                  384                 1152 
## CMP Irf8-GFP+ MHCII+     Unsorted myeloid 
##                 1152                 3072

PaulHSCData <- logNormCounts(PaulHSCData)

# Subset to top 10% HVG
chosen_features <-
  getTopHVGs(modelGeneVar(PaulHSCData, PaulHSCData$Seq_batch_ID),
             n = ceiling(nrow(PaulHSCData) / 10))
PaulHSCData <- PaulHSCData[chosen_features, ]

# Subset to two smallest batches
PaulHSCData <- PaulHSCData[, colData(PaulHSCData)$Seq_batch_ID %in% c("SB17", "SB20")]

# Create default named factors for batches and cell clusters
colData(PaulHSCData)$Batch <- factor(colData(PaulHSCData)$Seq_batch_ID)
colData(PaulHSCData)$Cluster <- factor(colData(PaulHSCData)$Batch_desc)

PaulHSCData <- runPCA(PaulHSCData)
plotPCA(PaulHSCData, color_by = "Batch")

plotPCA(PaulHSCData, color_by = "Cluster")

PaulHSCData <- runTSNE(PaulHSCData)
plotTSNE(PaulHSCData, color_by = "Batch")

plotTSNE(PaulHSCData, color_by = "Cluster")

PaulHSCData <- runUMAP(PaulHSCData)
plotUMAP(PaulHSCData, color_by = "Batch")

plotUMAP(PaulHSCData, color_by = "Cluster")

Zilionis Lung Data

ZilionisLungData <- ZilionisLungData('mouse')
ZilionisLungData

## class: SingleCellExperiment 
## dim: 28205 17549 
## metadata(0):
## assays(1): counts
## rownames(28205): 0610007P14Rik 0610009B22Rik ... mt-Nd5 mt-Nd6
## rowData names(0):
## colnames(17549): bc0001 bc0002 ... bc1087 bc1088
## colData names(12): Library Barcode ... Major cell type Minor subset
## reducedDimNames(1): SPRING
## mainExpName: NULL
## altExpNames(0):

table(Batch = colData(ZilionisLungData)$`Library prep batch`)

## Batch
## round1_20151128 round2_20151217 round3_20160313 
##            4339            3639            7961

table(Cluster = colData(ZilionisLungData)$`Major cell type`)

## Cluster
##     B cells   Basophils     MoMacDC Neutrophils    NK cells         pDC 
##        2813          34        2397        8022         630          62 
##     T cells 
##        1981

ZilionisLungData <- logNormCounts(ZilionisLungData)

# Subset to top 10% HVG
chosen_features <-
  getTopHVGs(
    modelGeneVar(ZilionisLungData, ZilionisLungData$`Library prep batch`),
    n = ceiling(nrow(ZilionisLungData) / 10)
  )

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

ZilionisLungData <- ZilionisLungData[chosen_features, ]

# Subset to two smallest batches
ZilionisLungData <- ZilionisLungData[, colData(ZilionisLungData)$`Library prep batch` %in% c("round1_20151128", "round2_20151217")]

# Create default named factors for batches and cell clusters
colData(ZilionisLungData)$Batch <- factor(colData(ZilionisLungData)$`Library prep batch`)
colData(ZilionisLungData)$Cluster <- factor(colData(ZilionisLungData)$`Major cell type`)

ZilionisLungData <- runPCA(ZilionisLungData)
plotPCA(ZilionisLungData, color_by = "Batch")

plotPCA(ZilionisLungData, color_by = "Cluster")

ZilionisLungData <- runTSNE(ZilionisLungData)
plotTSNE(ZilionisLungData, color_by = "Batch")

plotTSNE(ZilionisLungData, color_by = "Cluster")

ZilionisLungData <- runUMAP(ZilionisLungData)
plotUMAP(ZilionisLungData, color_by = "Batch")

plotUMAP(ZilionisLungData, color_by = "Cluster")

Wu Kidney Data

WuKidneyData <- WuKidneyData()
WuKidneyData

## class: SingleCellExperiment 
## dim: 18249 17542 
## metadata(0):
## assays(1): counts
## rownames(18249): mt-Cytb mt-Nd6 ... Gm44613 Gm38304
## rowData names(0):
## colnames(17542): sCellDropseq_AAAAAGAAGGAC sCellDropseq_AAAACTACCTTC
##   ... UUO_TTGCCGTCACAAGACG UUO_TTTGTCATCTGCTGTC
## colData names(2): Technology Status
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

table(Batch = colData(WuKidneyData)$Technology)

## Batch
##     DroNcSeq sCellDropseq     sNuc-10x  sNucDropseq 
##         2769         3531         8231         3011

table(Cluster = colData(WuKidneyData)$Status)

## Cluster
## disease healthy 
##    6147   11395

WuKidneyData <- logNormCounts(WuKidneyData)

# Subset to top 10% HVG
chosen_features <-
  getTopHVGs(modelGeneVar(WuKidneyData, WuKidneyData$Technology),
             n = ceiling(nrow(WuKidneyData) / 10))
WuKidneyData <- WuKidneyData[chosen_features, ]

# Subset to two smallest batches
WuKidneyData <- WuKidneyData[, colData(WuKidneyData)$Technology %in% c("sCellDropseq", "sNuc-10x")]

# Create default named factors for batches and cell clusters
colData(WuKidneyData)$Batch <- factor(colData(WuKidneyData)$Technology)
colData(WuKidneyData)$Cluster <- factor(colData(WuKidneyData)$Status)

WuKidneyData <- runPCA(WuKidneyData)
plotPCA(WuKidneyData, color_by = "Batch")

plotPCA(WuKidneyData, color_by = "Cluster")

WuKidneyData <- runTSNE(WuKidneyData)
plotTSNE(WuKidneyData, color_by = "Batch")

plotTSNE(WuKidneyData, color_by = "Cluster")

WuKidneyData <- runUMAP(WuKidneyData)
plotUMAP(WuKidneyData, color_by = "Batch")

plotUMAP(WuKidneyData, color_by = "Cluster")

Session Information

sessionInfo()

## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ensembldb_2.22.0            AnnotationFilter_1.22.0    
##  [3] GenomicFeatures_1.50.4      AnnotationDbi_1.60.0       
##  [5] scRNAseq_2.12.0             scran_1.26.2               
##  [7] scater_1.26.1               scuttle_1.8.4              
##  [9] splatter_1.22.1             SingleCellExperiment_1.20.0
## [11] SummarizedExperiment_1.28.0 Biobase_2.58.0             
## [13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
## [15] IRanges_2.32.0              S4Vectors_0.36.2           
## [17] BiocGenerics_0.44.0         MatrixGenerics_1.10.0      
## [19] matrixStats_0.63.0          ggthemes_4.2.4             
## [21] cowplot_1.1.1               lubridate_1.9.2            
## [23] forcats_1.0.0               stringr_1.5.0              
## [25] dplyr_1.1.0                 purrr_1.0.1                
## [27] readr_2.1.4                 tidyr_1.3.0                
## [29] tibble_3.1.8                ggplot2_3.4.1              
## [31] tidyverse_2.0.0            
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.4.1               AnnotationHub_3.6.0          
##   [3] BiocFileCache_2.6.1           igraph_1.3.5                 
##   [5] lazyeval_0.2.2                BiocParallel_1.32.5          
##   [7] digest_0.6.30                 htmltools_0.5.4              
##   [9] viridis_0.6.2                 fansi_1.0.4                  
##  [11] magrittr_2.0.3                checkmate_2.1.0              
##  [13] memoise_2.0.1                 ScaledMatrix_1.6.0           
##  [15] cluster_2.1.4                 tzdb_0.3.0                   
##  [17] limma_3.54.2                  Biostrings_2.66.0            
##  [19] vroom_1.6.1                   timechange_0.2.0             
##  [21] prettyunits_1.1.1             rmdformats_1.0.4             
##  [23] colorspace_2.1-0              blob_1.2.3                   
##  [25] rappdirs_0.3.3                ggrepel_0.9.2                
##  [27] xfun_0.37                     crayon_1.5.2                 
##  [29] RCurl_1.98-1.10               jsonlite_1.8.4               
##  [31] glue_1.6.2                    gtable_0.3.1                 
##  [33] zlibbioc_1.44.0               XVector_0.38.0               
##  [35] DelayedArray_0.24.0           BiocSingular_1.14.0          
##  [37] scales_1.2.1                  DBI_1.1.3                    
##  [39] edgeR_3.40.2                  Rcpp_1.0.9                   
##  [41] progress_1.2.2                viridisLite_0.4.1            
##  [43] xtable_1.8-4                  dqrng_0.3.0                  
##  [45] bit_4.0.5                     rsvd_1.0.5                   
##  [47] metapod_1.6.0                 httr_1.4.5                   
##  [49] FNN_1.1.3.1                   ellipsis_0.3.2               
##  [51] farver_2.1.1                  pkgconfig_2.0.3              
##  [53] XML_3.99-0.13                 uwot_0.1.14                  
##  [55] sass_0.4.5                    dbplyr_2.3.1                 
##  [57] locfit_1.5-9.7                utf8_1.2.2                   
##  [59] labeling_0.4.2                tidyselect_1.2.0             
##  [61] rlang_1.0.6                   later_1.3.0                  
##  [63] munsell_0.5.0                 BiocVersion_3.16.0           
##  [65] tools_4.2.2                   cachem_1.0.7                 
##  [67] cli_3.4.1                     generics_0.1.3               
##  [69] RSQLite_2.3.0                 ExperimentHub_2.6.0          
##  [71] evaluate_0.20                 fastmap_1.1.0                
##  [73] yaml_2.3.7                    knitr_1.42                   
##  [75] bit64_4.0.5                   KEGGREST_1.38.0              
##  [77] sparseMatrixStats_1.10.0      mime_0.12                    
##  [79] xml2_1.3.3                    biomaRt_2.54.0               
##  [81] compiler_4.2.2                rstudioapi_0.14              
##  [83] beeswarm_0.4.0                filelock_1.0.2               
##  [85] curl_5.0.0                    png_0.1-8                    
##  [87] interactiveDisplayBase_1.36.0 statmod_1.5.0                
##  [89] bslib_0.4.2                   stringi_1.7.12               
##  [91] highr_0.10                    lattice_0.20-45              
##  [93] bluster_1.8.0                 ProtGenerics_1.30.0          
##  [95] Matrix_1.5-3                  vctrs_0.5.2                  
##  [97] pillar_1.8.1                  lifecycle_1.0.3              
##  [99] BiocManager_1.30.20           jquerylib_0.1.4              
## [101] RcppAnnoy_0.0.20              BiocNeighbors_1.16.0         
## [103] bitops_1.0-7                  irlba_2.3.5.1                
## [105] rtracklayer_1.58.0            httpuv_1.6.8                 
## [107] BiocIO_1.8.0                  R6_2.5.1                     
## [109] bookdown_0.32                 promises_1.2.0.1             
## [111] gridExtra_2.3                 vipor_0.4.5                  
## [113] codetools_0.2-19              rjson_0.2.21                 
## [115] withr_2.5.0                   GenomicAlignments_1.34.0     
## [117] Rsamtools_2.14.0              GenomeInfoDbData_1.2.9       
## [119] parallel_4.2.2                hms_1.1.2                    
## [121] grid_4.2.2                    beachmat_2.14.0              
## [123] rmarkdown_2.20                DelayedMatrixStats_1.20.0    
## [125] Rtsne_0.16                    shiny_1.7.4                  
## [127] ggbeeswarm_0.7.1              restfulr_0.0.15